Networks based on the IEEE 802.11 suite of protocols are affordable for most organizations and now ubiquitous in most metropolitan areas. The ubiquity of the IEEE 802.11 infrastructure also makes these technologies an attractive proposition for indoor locations. These technologies have been deployed indoors in small to large local environments such as cafes, hotels, malls, warehouses and factory floors, and in wider deployments such as university campuses, airports, train stations, public buildings, and hospitals. The ability to determine and track the physical location of wireless and tagged devices in the smaller and larger-scale wireless local area networks (“WLANs”) leads to many interesting applications, particularly, with regard to the tracking of people, tagged assets, robots, and the like.
There is currently a growing interest in Location-Based Services (“LBS”). LBS are pervasive computing services that adapt to a user's location and situation. Location serves as a critical input for these applications. Pervasive computing represents the concept of “computing everywhere and anytime”, making the computing technology details transparent to the users. Situation-awareness is a desirable and important feature of the systems in pervasive computing environments. A situation-aware system can sense the contexts, such as location, time, noise level, and available resources in order to recognize the current situation.
Context is any information that can be used to characterize the situation of an entity. An entity is a person, place, or object that is considered relevant to the interaction between a user and an application. An example of a context is a computing context, which includes network connectivity, communication costs, communication bandwidth, and nearby resources such as printers, displays and workstations. Other examples include user context (such as user profile, location, people nearby, or a current social situation), physical context (e.g., lighting, noise levels, traffic conditions and temperature), and time context (such as time of a day, season of the year, etc.).
Location is one of the most important and frequently used contexts. With LBS, an application determines the location of a client and shapes the information accordingly. Very often, where you are determines what you do. A worker in a company tends to eat in the cafeteria, conducts experiments in the lab, and does normal work and research at his or her desk. Applications can use this context to adapt behavior.
Indoor knowledge-based trips, such as visits to museums, art galleries and exhibitions, botanical gardens, and zoos are getting more popular. Handheld devices such as Personal Digital Assistants (PDAs) have become more functional and suitable to play the role of mobile learning platforms for multimedia applications. A PDA can display information on an exhibit in a museum or art gallery according to a visitor's current location. As a visitor walks up to a specific exhibit, the system retrieves the corresponding multimedia narration of that exhibit automatically.
Some other important examples of LBS are finding the location of particular persons or objects (for instance in case of emergency), tracking and watching of active badges or tags (for instance in high-security areas, airports, storage halls), child tracking, medical alert, inventory management, workforce management, navigation, lone worker monitoring, demand-responsive transport, etc. Another interesting application is to accurately determine the position of a client in a WLAN as a basis for applying new techniques for access control and system security.
A security architecture can use such location information to offer services such as position based access policies, and Virtual LANs (VLANs). Another example application is Conference Assistant, which matches conference schedules, topics of presentations, user's location, and user's research interests to suggest the presentations to attend. This can be extended to other reminder services. Another example is Call Forwarding where the location context is determined and the phone calls to the destination user's nearest phone. Finally, Teleporting relates to “Follow me” computing where the user interface is dynamically mapped onto the resources of the surrounding computer and communication facilities.
Systems specifically oriented towards positioning and navigation (i.e., Global Positioning System (“GPS”) or Galileo systems) and location systems which operate over cellular networks like Global System for Mobile communication (“GSM”) and Universal Mobile Telecommunication System (“UMTS”) already exists. However, these systems do not work properly and/or suffer from large location errors when used indoors. In particular, difficulties with GPS positioning usually occur in urban canyons (city cores) and indoors, where it is difficult or impossible to acquire the necessary number of satellites for a position computation. GPS receivers require an unobstructed view of the sky, so they are used only outdoors and they often do not perform well within forested areas or near tall buildings. Positioning becomes more difficult at medium- to deep-indoors, electrically noisy indoor scenarios, subterranean places like underground parking, etc. Since these locations systems are inefficient for indoor environments, alternative positioning technologies are required. To date the solutions proposed for indoor environments have not been particularly accurate. Current wireless LAN-based location systems suffer from the noisy characteristics of the wireless channel and multipath distortion. As a result, the time-of-flight (“TOF”) is highly variant due to the impact of multipath errors.
Most of the location techniques used in wireless environments are based on received signal strength (“RSS”), angle-of arrival (“AOS”), time-of-arrival (“TOA”), and time-difference-of arrival (“TDOA”) methods. All of these methods require line-of-sight (“LOS”) communication paths. Unfortunately, in real networks, such direct paths rarely exist. Quite often, the signal received is either a combination of the LOS signal and multipath signals or consists of only multipath signals. In either case, there exist so-called non-line-of-sight (“NLOS”) propagation errors which tend to be the dominant error component when compared to receiver noise. In particular, NLOS errors tend to be very large in wireless indoor locations systems, and hence dramatically degrade positioning accuracy. As a result, these errors must be mitigated when determining the position of clients in wireless indoor environments. NLOS error mitigation techniques must be applied in the location process to remove or minimize the deleterious effects of these errors on positioning accuracies
Systems specifically oriented towards positioning and navigation (i.e., GPS or Galileo systems) and location systems which operate over cellular networks like GSM and UMTS already exist. However, these systems do not work properly and/or suffer from large location errors when used indoors. In particular, difficulties with GPS positioning usually occur in urban canyons (city cores) and indoors, where it is difficult or impossible to acquire the necessary number of satellites for a position computation. GPS receivers require an unobstructed view of the sky, so they are used only outdoors and they often do not perform well within forested areas or near tall buildings. Positioning becomes more difficult at medium- to deep-indoors, electrically noisy indoor scenarios, subterranean places like underground parking, etc. Since these locations systems are inefficient for indoor environments, alternative positioning technologies are required. To date, the solutions proposed for indoor environments have not been particularly accurate. Current wireless LAN-based location systems suffer from the noisy characteristics of the wireless channel and multipath distortion, and the TOF is highly variant due to the impact of multipath errors
Therefore, what is needed is a system and method for the indoor position location of clients in WLANs that do not suffer from the above-described problems.